CathAction: A Benchmark for Endovascular Intervention Understanding
This dataset addresses a problem for researchers in medical computer vision by providing a benchmark to improve surgical safety and efficiency, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of comprehensive datasets for endovascular intervention understanding by introducing CathAction, a large-scale dataset with approximately 500,000 annotated frames for action understanding and collision detection, and 25,000 ground truth masks for segmentation.
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/.